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Electron–positron pair production via the nonlinear Bethe–Heitler effect in the combined fields of a bare nucleus and a high-intensity laser pulse is studied theoretically. The calculations are performed within the framework of strong-field quantum electrodynamics using a flat-top laser profile with raising and falling edges. This way, the dependence of the pair production process on the precise shape of the laser field is analyzed. Our approach allows us, in particular, to follow the evolution of the created particles’ energy spectra from ultra-short few-cycle pulses to the monochromatic infinite pulse-train limit. We show how the various portions of the pulse influence these spectra and determine conditions for which the outcome from a laser pulse closely resembles the predictions from monochromatic theory.

Monitoring marine phytoplankton is essential to understanding marine ecosystems functioning, especially in productive regions like the English Channel. This study applied high‐throughput sequencing (HTS) and automated pulse shape‐recording flow cytometry (PSR FCM) to investigate the spatial and seasonal variability of phytoplankton diversity in French waters of the English Channel during the ECOPEL cruises in April (spring) and July (summer) 2018. Our findings revealed significant seasonal shifts in size, structure, total red fluorescence (FLR, a biomass proxy) and community composition. PSR FCM provided high‐resolution size class discrimination, revealing an increase in picoeukaryote abundance and lower FLR in summer compared to spring. HTS enabled detailed taxonomic insights: in spring, picoeukaryotes (e.g., Ostreococcus) dominated in the Western English Channel, except in Finistère/Celtic Seas, where microphytoplankton represented the majority of reads. Nanoeukaryotes (Phaeocystis) dominated in the Eastern English Channel. In summer, diversity increased, with co‐dominance of picoeukaryotes (Micromonas, Bathycoccus, Ostreococcus), microphytoplankton (Chaetoceros, Leptocylindrus, Guinardia) and nanoeucaryotes (Teleaulax, Gephyrocapsa) in the Bay of Seine. Beyond a pronounced west‐east disparity, the Bay of Seine exhibited remarkable taxonomic and functional diversity, with high local contribution to beta diversity (LCBD) values in both seasons. Diversity patterns were strongly influenced by temperature and nutrient concentrations (phosphate, nitrogen), with secondary influences from salinity and turbidity. PSR FCM further revealed sub‐mesoscale variability in abundance and size structure, complementing the mesoscale patterns observed through HTS. This study highlights the importance of integrating both methods to capture fine‐scale phytoplankton dynamics and high‐resolution diversity, thereby enhancing ecosystem management, espcecially in nutrient‐sensitive, productive marine regions. The preprint of the manuscript is available on Authorea (DOI: 10.22541/au.174523338.82769110/v1) and relayed by Archimer (https://archimer.ifremer.fr/doc/00951/106275/). By combining flow cytometry and high‐throughput sequencing, we reveal seasonal and spatial variations in the size, composition, and diversity of phytoplankton in the English Channel, highlighting the dynamics of pico‐, nano‐, and microphytoplankton and environmental factors, with the Seine Bay as a hotspot of taxonomic and functional diversity.

In this work, we report the fabrication and characterization of single-film and double-film composite epitaxial garnet structures based on single-crystalline films (SCFs) and bulk single-crystal (SC) scintillators for enhanced α–γ discrimination in mixed radiation fields. These composite scintillators consist of TbAG:Ce and YAG:Ce SCFs grown by liquid-phase epitaxy (LPE) on Czochralski-grown Gd3Ga2.5Al2.5O12 (GAGG:Ce) bulk SC substrates. Single- and double-film architectures were designed to optimize the energy absorption and pulse-shape discrimination (PSD) performance for low-penetrating α-particles and high-energy γ-rays. Energy calibration was performed using different γ-ray sources (57Co, 51Cr, and 137Cs), enabling the conversion of detector signals to a calibrated electron-equivalent energy scale (keVee). Integration gates were systematically optimized, yielding maximum figures of merit (FOM) of 1.4 for the GAGG:Ce SC substrate, 1.9 for the single-film composite, and 5.0 for the double-film composite, demonstrating a progressive improvement in α–γ discrimination with increasing structural complexity. Two-dimensional PSD density maps reveal well-separated α and γ events, with the highest separation observed for the double-film composite. These results indicate that the engineering of LPE-grown composites provides tunable scintillation decay profiles, enhanced temporal separation, and increased light yields, making them promising candidates for applications such as mixed radiation field detection, dosimetry, and radiation monitoring.

This paper studies the influence of bias voltage on pulse waveform, energy resolution, and particle discrimination performance to optimize the performance of n / γ pulse shape discrimination (PSD) for plastic scintillator coupled with SiPM arrays. Experiments were carried out with the EJ‐276 plastic scintillator detection system based on 2 × 2 SiPM arrays in gamma sources ( 22 Na, 60 Co, 137 Cs) and neutron source ( 252 Cf). And the pulse amplitude, shape, and the dispersion degree of the pulse waveform with bias voltage in the range of 25.8–28.8 V were analyzed. Gauss expansion method and PSD method based on the charge integration were used to obtain accurate energy resolution and PSD parameter, respectively. The results showed that energy resolution and PSD performance improved firstly and then deteriorated as bias voltage increases, and the optimum bias voltage for both energy resolution and PSD performance was 27.6 V. Within the bias voltage range of 25.8–28.8 V, the energy resolutions at the optimal bias voltage were improved by 25.17% (0.341 MeV for 22 Na source) and 22.24% (1.062 MeV for 22 Na source). Additionally, the PSD performance for n / γ discrimination was improved by 19.6%.

Pulse shaping is a powerful tool for mitigating implosion instabilities in direct-drive inertial confinement fusion (ICF). However, the high-dimensional and nonlinear nature of implosions makes the pulse optimization quite challenging. In this research, we develop a machine-learning pulse shape designer to achieve high compression density and stable implosion. The facility-specific laser imprint pattern is considered in the optimization, which makes the pulse design more relevant. The designer is applied to the novel double-cone ignition scheme, and simulation shows that the optimized pulse increases the areal density expectation by 16% in one dimension, and the clean-fuel thickness by a factor of four in two dimensions. This pulse shape designer could be a useful tool for direct-drive ICF instability control.

In fast neutron multiplicity counting measurement, misclassification of γ signals and loss of neutrons introduce significant measurement errors. To address these problems, machine learning (ML) algorithms were employed to improve the n / γ discrimination of liquid scintillators. A dual-scintillator time-of-flight device combined with charge comparison (CC) method was used to select reliable datasets from the D-T neutron generator. Decision Tree, Random Forest, and Back-Propagation Neural Network (BPNN) were developed and compared with the CC method. The CC method and ML algorithms were validated using 137 Cs sources. The results showed that the ML algorithms had effective n / γ discrimination capabilities. The BPNN exhibited the highest DER γ (1.26%) and DER n (1.64%) discrimination performance, which reduced neutron loss and γ misclassification. In addition, the trained BPNN was used in practical measurement.

In this work, we present a study of newly developed two-layered composite scintillators based on epitaxial structures of garnet compounds for the simultaneous registration of different components of mixed radiation fluxes, and we evaluate their α/β/γ discrimination performance. The composite scintillators under study were doubly layered structures composed of TbAG:Ce or TbAG:Ce,Mg single-crystalline film grown onto Czochralski-grown GAGG:Ce single-crystal substrates using the liquid-phase epitaxy (LPE) method. The spectrometry measurements were performed with four different radioactive sources: 137Cs (emitting 661.6-keV γ rays), 241Am (5.5-MeV α particles and 59.5-keV γ rays), 90Sr (β particles with energies up to 2 MeV), and 14C (β particles with energies up to 156 keV). The pulse-height spectra (PHS) were recorded with a shaping time of 10 μs in an amplifier due to the presence of long scintillation components in the tested samples. Scintillation time profiles were measured under excitation of 661.6-keV γ rays, 5.5-MeV α particles, and β particles from 90Sr/90Y and 14C. Both types of TbAG:Ce film/GAGG:Ce substrate and TbAG:Ce,Mg film/GAGG:Ce substrate composites show good ability for the simultaneous registration of the mentioned components in the mixed radiation field with very reasonable Figure-of-Merit values: FoM(τ) greater than 0.2 and FoM(PSD) greater than 1.0.

Pulse shape discrimination (PSD) is at the core of radioactive particles monitoring. Conventional PSD methods are geared towards low event rates, and struggle in the presence of pileups resulting from high rate. In this work we develop a PSD algorithm that combines classic approaches with deep learning techniques, that is highly suitable for coping with the dramatic challenges associated with classifying pulses in high event rates. Common PSD algorithms for high event rates limit their research to two piled-up pulses. Our algorithm is designed and tested under severe pileup conditions, where three or more pulses were piled-up. We tested the algorithm on simulated data based on Cs 2 LiYCl6:Ce (CLYC) based detector pulse shapes and compare its performance to both traditional PSD algorithms and data-driven deep neural network (DNN) based algorithms. In high event rates, ranging up to 10 Mcps, the algorithm demonstrates up to 8 times fewer miss-classifications than the traditional normalized cross-correlation (NCC) approach, and up to 1.7 times fewer miss-classifications than a purely data-driven DNN-aided method.

We demonstrate the use of a convolutional neural network to perform neutron-gamma pulse shape discrimination, where the only inputs to the network are the raw digitised silicon photomultiplier signals from a dual scintillator detector element made of 6Li F:ZnS(Ag) scintillator and PVT plastic. A realistic labelled dataset was created to train the network by exposing the detector to an AmBe source, and a data-driven method utilising a separate photomultiplier tube was used to assign labels to the recorded signals. This approach is compared to the charge integration and continuous wavelet transform methods and a simpler artificial neural net. It is found to provide superior levels of discrimination, achieving an area under the curve of 0.996 ± 0.003. We find that the neural network is capable of extracting interpretable features directly from the raw data. In addition, by visualising the high-dimensional representations of the network with the t-SNE algorithm, we discover that not only is this method robust to minor mislabeling of the training dataset but that it is possible to identify an underlying substructure within the signals that goes beyond the original labelling. This technique could be utilised to explore and cluster complex, raw detector data in a novel way that may reveal more insights than standard analysis methods.

We developed an analog pulse shape discrimination (PSD) topology based on the well-established charge integration (CI) method, featuring two novel functional blocks beneficial for high event rate operation. The topology is designed for high-speed scintillators. The demonstrated analog design is potentially better suited than digital methods, when considering both processing time and power consumption aspects. The topology was tested using both experimental alpha and beta pulses from a plastic scintillator with a layer of ZnS(Ag) coupled to a PMT, and a fast digital emulator to simulate controlled high event rate scenarios. The discrimination capabilities of the topology were optimized and evaluated using a traditional figure of merit (FoM) approach. The topology achieved over 99% correct classifications when evaluated using the experimental pulses recorded. Additionally, the dedicated blocks resulted in a fourfold reduction in miss-classifications of slow pulses at an event rate of 100 kcps of fast pulses, while also providing a dynamic deadtime proportional to the pulse charge.